CN110059369A - A kind of analysis method of regional vegetation covering variation and climatic factor time-lag effect - Google Patents
A kind of analysis method of regional vegetation covering variation and climatic factor time-lag effect Download PDFInfo
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- CN110059369A CN110059369A CN201910241593.3A CN201910241593A CN110059369A CN 110059369 A CN110059369 A CN 110059369A CN 201910241593 A CN201910241593 A CN 201910241593A CN 110059369 A CN110059369 A CN 110059369A
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Abstract
The invention discloses a kind of regional vegetation covering variation and the analysis method of climatic factor time-lag effect, specifically: collection research regional weather office year precipitation data, year temperature record and year NDVI remotely-sensed data；A year vegetation coverage time series raster data, annual rainfall time series raster data and year temperature time series raster data is calculated；By vegetation coverage year by year, year by year rainfall, temperature time series raster data is separately converted to the numeric data of time series textual form year by year；Be calculated vegetative coverage to the lag time limit of the time-lag effect of precipitation and temperature, reach correlation, most strong time-lag effect that lag year prescribes a time limit when the lag time limit and most strong time-lag effect when correlation.The analysis method can provide scientific basis by the vegetation variation and climatic factor time-lag effect of the quick and easy calculating survey region of less data for Regional Eco-construction decision.
Description
Technical field
The invention belongs to regional vegetation covering variations and climatic similarity evaluation method technical field, are related to a kind of regional vegetation
The analysis method of covering variation and climatic factor time-lag effect.
Background technique
Since 1999, vegetation recovery engineering increases, and Vegetation of China situation has apparent variation.Vegetation is earth's surface
The energy exchange of the ecosystem, water circulation, carbon cycle, biogeochemical cycle important medium, vegetation maintaining region gas
It waits stablize etc. and plays particularly significant effect, the key that vegetation dynamic changes research has become global ecological environment variation is asked
One of topic.Many studies have shown that coupling relationship is with climatic factor, there are time-lag effect, i.e. influence of the climatic factor to coupling relationship
There are temporal hysteresis.Therefore, vegetation variation is studied and its to the response of climate change to survey region ecology
Environmental change is of great significance.At present about regional vegetation covering variation with the method for climatic factor time-lag effect also compared with
It is few.Therefore it is highly important for proposing that a kind of regional vegetation covering changes with the analysis method of climatic factor time-lag effect.
Summary of the invention
The object of the present invention is to provide a kind of analysis method of regional vegetation covering variation and climatic factor time-lag effect, solutions
Determined existing regional vegetation covering variation and climatic factor time-lag effect analysis method complexity height, range of value is small asks
Topic.
The technical scheme adopted by the invention is that a kind of analysis of regional vegetation covering variation and climatic factor time-lag effect
Method is specifically implemented according to the following steps:
Step 1, it determines survey region, collects weather station year precipitation data, year temperature record and year NDVI remotely-sensed data；
Step 2, according to the year NDVI remotely-sensed data being collected into, a year vegetation coverage time series grid number is calculated
According to being calculated by space interpolation same with year vegetation coverage according to weather station year precipitation data, year temperature record is collected into
The research area annual rainfall time series raster data and year temperature time series raster data of sample precision；
Step 3, on the basis of step 2, by ArcGIS software will year by year vegetation coverage time series raster data,
Rainfall time series raster data and temperature time series raster data is separately converted to point data year by year year by year, then pass through
ArcGIS software respectively exports the attribute list of time series dot file, obtain year by year vegetation coverage time series, drop year by year
Rainfall time series and the year by year numeric data of temperature time series textual form；
Step 4, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through meter
The programming of calculation machine, calculates separately the lag time limit for obtaining vegetative coverage to the time-lag effect of precipitation and temperature, by vegetative coverage to drop
The lag time limit of the time-lag effect of water and temperature is respectively converted into raster data；
Step 5, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form pass through meter
The programming of calculation machine calculates separately to obtain vegetative coverage and reaches the correlation prescribed a time limit in lag year to precipitation and temperature, calculated result is planted
It is capped the correlation that lag year prescribes a time limit is reached to precipitation and temperature to be respectively converted into raster data；
Step 6, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through meter
The programming of calculation machine, calculates separately to obtain lag time limit when most strong time-lag effect of the vegetative coverage to precipitation and temperature, ties calculating
Lag time limit when fruit vegetative coverage is to the most strong time-lag effect of precipitation and temperature is respectively converted into raster data；
Step 7, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through meter
The programming of calculation machine, calculates separately to obtain correlation when most strong time-lag effect of the vegetative coverage to precipitation and temperature, by calculated result
Correlation when vegetative coverage is to the most strong time-lag effect of precipitation and temperature is respectively converted into raster data.
The features of the present invention also characterized in that
In step 1, year NDVI remotely-sensed data is obtained using maximum value synthetic method, specifically:
By ArcGIS software Spatial Analyst Tools module Local tool Cell Statistics according to most
Big value synthetic method gradually obtains the moon, season NDVI time series raster data, finally obtains a year NDVI time series raster data,
Pass through ArcGIS software Spatial Analyst Tools module Map Algebra tool Raster Calculator again for year
NDVI time series raster data is converted to vegetation coverage time series raster data.
Step 2 specifically:
Step 2.1, year vegetation coverage data pass through ArcGIS software Spatial Analyst by NDVI data
Year NDVI time series raster data is converted to vegetation and covered by Tools module Map Algebra tool Raster Calculator
Cover degree time series raster data；It is calculated using formula (1) formula:
F=(NDVI-NDVImin)/(NDVImax-NDVImin) (1)；
In formula (1), f is vegetation coverage, and NDVI is the vegetation index of pixel, NDVImaxAnd NDVIMinIt is research area respectively
The maximum value and minimum value of NDVI；
Step 2.2, according to weather station year precipitation data, year temperature record is collected into, pass through ArcGIS software Spatial
In Analyst Tools module gram in golden spatial interpolation methods calculate and dropped with the research area of year vegetation coverage same accuracy year
Rainfall time series raster data and year temperature time series raster data.
In step 4, year vegetative coverage to the calculation method of the time lag time limit of rainfall and temperature, specifically:
Step 4.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage to rainfall when
It is limited in stagnant year 0 year；If inspection result is not significant, by R language to the corresponding annual rainfall of the same grid, postpone 1 year
The numeric data of year vegetative coverage time series textual form carries out Pearson came correlation test, should if inspection result is significant
Grid year, vegetative coverage was limited to the time lag year of rainfall 1 year；If inspection result is not still significant, corresponding to the same grid
Annual rainfall, postpone N year vegetative coverage time series textual form numeric data carry out Pearson came correlation test, push away
Afterwards after N until inspection result is significant, then grid year vegetative coverage is limited to N to the time lag year of rainfall, when wherein N is up to
Between the length of sequence subtract one；If inspection result is not significant always, grid year vegetative coverage time-lag effect is not present to rainfall；
Step 4.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating such as step 4.1, obtain each grid year vegetative coverage to time lag year of rainfall
Limit；
Step 4.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data is carried out the calculating such as step 4.2, obtain each grid year vegetative coverage to the time lag time limit of temperature；
Step 4.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
In step 5, year vegetative coverage to rainfall and temperature reach lag year prescribe a time limit correlation calculation method, specifically
Are as follows:
Step 5.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
Lag the related coefficient that the correlation that year prescribes a time limit is Pearson came correlation test；If inspection result is not significant, pass through R language
To the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series textual form numeric data carry out skin
Your inferior correlation test, if inspection result is significant, grid year vegetative coverage reach the correlation prescribed a time limit in lag year to rainfall
For the related coefficient of Pearson came correlation test；If inspection result is not still significant, the same grid corresponding year is dropped
Rain, postpone N year vegetative coverage time series textual form numeric data carry out Pearson came correlation test, postpone N
Afterwards until inspection result is significant, then it is that Pearson came is related that grid year vegetative coverage, which reaches the correlation that lag year prescribes a time limit to rainfall,
Property the related coefficient examined, the length that wherein N is up to time series subtracts one；If inspection result is not significant always, the grid
Time-lag effect is not present to rainfall in year vegetative coverage；
Step 5.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating of step 5.1, and the then grid year vegetative coverage for obtaining each grid reaches rainfall
The correlation prescribed a time limit to lag year；
Step 5.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data carries out the calculating such as step 5.2, obtains each grid year vegetative coverage and reaches the correlation prescribed a time limit in lag year to temperature
Property；
Step 5.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
In step 6, year vegetative coverage to rainfall and temperature most strong time-lag effect when lag the time limit calculation method, specifically
Are as follows:
Step 6.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
The related coefficient that the correlation that year prescribes a time limit is Pearson came correlation test is lagged, then time lag of grid year vegetative coverage to rainfall
Year is limited to 0 year；Then, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series
The numeric data of textual form carries out Pearson came correlation test, if inspection result is significant, grid year, vegetative coverage was to drop
Rain reaches the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test, then grid year vegetative coverage is to rainfall
Be limited in time lag year 1 year；Then, to the corresponding annual rainfall of the same grid, postpone the year vegetative coverage time series text of N
The numeric data of form carries out Pearson came correlation test, postpones after N until inspection result is significant, then grid year vegetation is covered
It covers and the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test is reached to rainfall, then grid year vegetative coverage
N is limited to the time lag year of rainfall, the length that wherein N is up to time series subtracts one；It should in N number of calculated result finally, finding
Grid year vegetative coverage to rainfall occur hysteresis effect when the corresponding time lag time limit of maximum related coefficient, then be limited to the grid year
Lattice year vegetative coverage to rainfall most strong time-lag effect when the lag time limit；If inspection result is not significant always, which plants
It is capped that time-lag effect is not present to rainfall；
Step 6.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating of step 6.1, obtains each grid year vegetative coverage to the most strong time-lag effect of rainfall
When the lag time limit；
Step 6.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data carries out the calculating such as step 6.2, lag year when to each grid year vegetative coverage to temperature most strong time-lag effect
Limit；
Step 6.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
In step 7, year vegetative coverage to rainfall and temperature most strong time-lag effect when correlation calculation method are as follows:
Step 7.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
The related coefficient that the correlation that year prescribes a time limit is Pearson came correlation test is lagged, then time lag of grid year vegetative coverage to rainfall
Year is limited to 0 year；Then, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series
The numeric data of textual form carries out Pearson came correlation test, if inspection result is significant, grid year, vegetative coverage was to drop
Rain reaches the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test, then grid year vegetative coverage is to rainfall
Be limited in time lag year 1 year；Then, to the corresponding annual rainfall of the same grid, postpone the year vegetative coverage time series text of N
The numeric data of form carries out Pearson came correlation test, postpones after N until inspection result is significant, then grid year vegetation is covered
It covers and the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test is reached to rainfall, then grid year vegetative coverage
N is limited to the time lag year of rainfall, the length that wherein N is up to time series subtracts one；It should in N number of calculated result finally, finding
Grid year maximum related coefficient of vegetative coverage when rainfall occurring hysteresis effect, then the related coefficient is grid year vegetation
Correlation when covering to rainfall most strong time-lag effect；If inspection result is not significant always, grid year, vegetative coverage was to drop
Time-lag effect is not present in rain；
Step 7.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating of step 7.1, obtains each grid year vegetative coverage to the most strong time-lag effect of rainfall
When correlation；
Step 7.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data carries out the calculating such as step 7.2, correlation when to each grid year vegetative coverage to temperature most strong time-lag effect；
Step 7.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
The invention has the advantages that
The analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect of the invention, data are easy to obtain
Take, calculate it is simple, convenient, can be quickly to research area's vegetative coverage by obtaining survey region NDVI and precipitation, temperature record
Variation is analyzed with climatic factor time-lag effect, obtains research area different regions vegetation variation and climatic factor time lag year
Limit, delay-dependent criterion, most strong delay-dependent criterion, the most strong delay-dependent criterion time limit.
Detailed description of the invention
Fig. 1 is Ordos City vegetative coverage and rainfall time lag time limit spatial distribution map in example of the invention；
Fig. 2 is Ordos City vegetative coverage and rainfall delay-dependent criterion spatial distribution map in example of the invention；
Fig. 3 is Ordos City vegetative coverage and temperature time lag time limit spatial distribution map in example of the invention；
Fig. 4 is Ordos City vegetative coverage and temperature delay-dependent criterion spatial distribution map in example of the invention；
Fig. 5 is Ordos City vegetative coverage and time limit space after the most strong time-lag effect time lag of rainfall in example of the invention
Distribution map；
Fig. 6 is Ordos City vegetative coverage and correlation space point when rainfall most strong time-lag effect in example of the invention
Butut；
Fig. 7 is Ordos City vegetative coverage and time limit space after the most strong time-lag effect time lag of temperature in example of the invention
Distribution map；
Fig. 8 is Ordos City vegetative coverage and correlation space point when temperature most strong time-lag effect in example of the invention
Butut.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
A kind of analysis method of regional vegetation covering variation and climatic factor time-lag effect of the present invention, specifically according to following step
It is rapid to implement:
Step 1, it determines survey region, collects weather station year precipitation data, year temperature record and year NDVI remotely-sensed data,
It is synthesized by ArcGIS software Spatial Analyst Tools module Local tool Cell Statistics according to maximum value
Method gradually obtains the moon, season NDVI time series raster data, finally obtains a year NDVI time series raster data；Pass through again
When ArcGIS software Spatial Analyst Tools module Map Algebra tool Raster Calculator is by year NDVI
Between sequence trellis data be converted to vegetation coverage time series raster data；
Step 2, according to the year NDVI remotely-sensed data being collected into, a year vegetation coverage time series grid number is calculated
According to being calculated by space interpolation same with year vegetation coverage according to weather station year precipitation data, year temperature record is collected into
The research area annual rainfall time series raster data and year temperature time series raster data of sample precision, specifically:
Step 2.1, year vegetation coverage data pass through ArcGIS software Spatial Analyst by NDVI data
Year NDVI time series raster data is converted to vegetation and covered by Tools module Map Algebra tool Raster Calculator
Cover degree time series raster data；It is calculated using formula (1) formula:
F=(NDVI-NDVImin)/(NDVImax-NDVImin) (1)；
In formula (1), f is vegetation coverage, and NDVI is the vegetation index of pixel, NDVImaxAnd NDVIMinIt is research area respectively
The maximum value and minimum value of NDVI；
Step 2.2, according to weather station year precipitation data, year temperature record is collected into, pass through ArcGIS software Spatial
In Analyst Tools module gram in golden spatial interpolation methods calculate and dropped with the research area of year vegetation coverage same accuracy year
Rainfall time series raster data and year temperature time series raster data；
Step 3, on the basis of step 2, by ArcGIS software will year by year vegetation coverage time series raster data,
Rainfall time series raster data and temperature time series raster data is separately converted to point data year by year year by year, then pass through
ArcGIS software respectively exports the attribute list of time series dot file, obtain year by year vegetation coverage time series, drop year by year
Rainfall time series and the year by year numeric data of temperature time series textual form；
Step 4, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through meter
The programming of calculation machine, calculates separately the lag time limit for obtaining vegetative coverage to the time-lag effect of precipitation and temperature, by vegetative coverage to drop
The lag time limit of the time-lag effect of water and temperature is respectively converted into raster data, specifically:
Step 4.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage to rainfall when
It is limited in stagnant year 0 year；If inspection result is not significant, by R language to the corresponding annual rainfall of the same grid, postpone 1 year
The numeric data of year vegetative coverage time series textual form carries out Pearson came correlation test, should if inspection result is significant
Grid year, vegetative coverage was limited to the time lag year of rainfall 1 year；If inspection result is not still significant, corresponding to the same grid
Annual rainfall, postpone N year vegetative coverage time series textual form numeric data carry out Pearson came correlation test, push away
Afterwards after N until inspection result is significant, then grid year vegetative coverage is limited to N to the time lag year of rainfall, when wherein N is up to
Between the length of sequence subtract one；If inspection result is not significant always, grid year vegetative coverage time-lag effect is not present to rainfall；
Step 4.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating such as step 4.1, obtain each grid year vegetative coverage to time lag year of rainfall
Limit；
Step 4.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data is carried out the calculating such as step 4.2, obtain each grid year vegetative coverage to the time lag time limit of temperature；
Step 4.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software；
Step 5, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form pass through meter
The programming of calculation machine calculates separately to obtain vegetative coverage and reaches the correlation prescribed a time limit in lag year to precipitation and temperature, calculated result is planted
It is capped the correlation that lag year prescribes a time limit is reached to precipitation and temperature to be respectively converted into raster data, specifically:
Step 5.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
Lag the related coefficient that the correlation that year prescribes a time limit is Pearson came correlation test；If inspection result is not significant, pass through R language
To the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series textual form numeric data carry out skin
Your inferior correlation test, if inspection result is significant, grid year vegetative coverage reach the correlation prescribed a time limit in lag year to rainfall
For the related coefficient of Pearson came correlation test；If inspection result is not still significant, the same grid corresponding year is dropped
Rain, postpone N year vegetative coverage time series textual form numeric data carry out Pearson came correlation test, postpone N
Afterwards until inspection result is significant, then it is that Pearson came is related that grid year vegetative coverage, which reaches the correlation that lag year prescribes a time limit to rainfall,
Property the related coefficient examined, the length that wherein N is up to time series subtracts one；If inspection result is not significant always, the grid
Time-lag effect is not present to rainfall in year vegetative coverage；
Step 5.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating of step 5.1, and the then grid year vegetative coverage for obtaining each grid reaches rainfall
The correlation prescribed a time limit to lag year.
Step 5.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data carries out the calculating such as step 5.2, obtains each grid year vegetative coverage and reaches the correlation prescribed a time limit in lag year to temperature
Property；
Step 5.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software；
Step 6, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through meter
The programming of calculation machine, calculates separately to obtain lag time limit when most strong time-lag effect of the vegetative coverage to precipitation and temperature, ties calculating
Lag time limit when fruit vegetative coverage is to the most strong time-lag effect of precipitation and temperature is respectively converted into raster data；
Step 6.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
The related coefficient that the correlation that year prescribes a time limit is Pearson came correlation test is lagged, then time lag of grid year vegetative coverage to rainfall
Year is limited to 0 year；Then, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series
The numeric data of textual form carries out Pearson came correlation test, if inspection result is significant, grid year, vegetative coverage was to drop
Rain reaches the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test, then grid year vegetative coverage is to rainfall
Be limited in time lag year 1 year；Then, to the corresponding annual rainfall of the same grid, postpone the year vegetative coverage time series text of N
The numeric data of form carries out Pearson came correlation test, postpones after N until inspection result is significant, then grid year vegetation is covered
It covers and the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test is reached to rainfall, then grid year vegetative coverage
N is limited to the time lag year of rainfall, the length that wherein N is up to time series subtracts one；It should in N number of calculated result finally, finding
Grid year vegetative coverage to rainfall occur hysteresis effect when the corresponding time lag time limit of maximum related coefficient, then be limited to the grid year
Lattice year vegetative coverage to rainfall most strong time-lag effect when the lag time limit；If inspection result is not significant always, which plants
It is capped that time-lag effect is not present to rainfall；
Step 6.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating of step 6.1, obtains each grid year vegetative coverage to the most strong time-lag effect of rainfall
When the lag time limit；
Step 6.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data carries out the calculating such as step 6.2, lag year when to each grid year vegetative coverage to temperature most strong time-lag effect
Limit；
Step 6.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software；
Step 7, on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, by
The numeric data of annual rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through meter
The programming of calculation machine, calculates separately to obtain correlation when most strong time-lag effect of the vegetative coverage to precipitation and temperature, by calculated result
Correlation when vegetative coverage is to the most strong time-lag effect of precipitation and temperature is respectively converted into raster data；
Step 7.1, by R language to the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Numeric data carry out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
The related coefficient that the correlation that year prescribes a time limit is Pearson came correlation test is lagged, then time lag of grid year vegetative coverage to rainfall
Year is limited to 0 year；Then, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series
The numeric data of textual form carries out Pearson came correlation test, if inspection result is significant, grid year, vegetative coverage was to drop
Rain reaches the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test, then grid year vegetative coverage is to rainfall
Be limited in time lag year 1 year；Then, to the corresponding annual rainfall of the same grid, postpone the year vegetative coverage time series text of N
The numeric data of form carries out Pearson came correlation test, postpones after N until inspection result is significant, then grid year vegetation is covered
It covers and the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test is reached to rainfall, then grid year vegetative coverage
N is limited to the time lag year of rainfall, the length that wherein N is up to time series subtracts one；It should in N number of calculated result finally, finding
Grid year maximum related coefficient of vegetative coverage when rainfall occurring hysteresis effect, then the related coefficient is grid year vegetation
Correlation when covering to rainfall most strong time-lag effect；If inspection result is not significant always, grid year, vegetative coverage was to drop
Time-lag effect is not present in rain；
Step 7.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series
The numeric data of textual form carries out the calculating of step 7.1, obtains each grid year vegetative coverage to the most strong time-lag effect of rainfall
When correlation；
Step 7.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form
Numeric data carries out the calculating such as step 7.2, correlation when to each grid year vegetative coverage to temperature most strong time-lag effect；
Step 7.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software；
Step 4, Pearson came correlation test calculation method is as follows in 5,6,7:
Pearson correlation coefficient is for measuring between two variable vegetation coverages and climatic factor (rainfall or temperature)
Correlation (linear correlation), for value between -1 and 1, the Pearson correlation coefficient between two variables is defined as two variables
Between covariance and standard deviation quotient, covariance and variance are estimated based on sample, available sample Pearson came phase
Relationship number r, as shown in formula (2):
In formula, XiFor 1 year vegetation coverage,For many years vegetation coverage mean value, YiFor the i-th year-climate factor values (drop
Rain or temperature),For many years climatic factor (rainfall or temperature) mean value；N is time length.
Embodiment
What using the analysis of Xijiang River vegetation variation and climatic factor time-lag effect, as example, the present invention will be described in detail
Analysis method is specifically implemented according to the following steps as shown in figures 1-8:
Step 1, basic data: 1 > Xijiang River NDVI data is collected first, during 2000 to 2013, space point
Resolution is 500 meters × 500 meters, is divided into 10 days, by Computer Network Information Center, Chinese Academy of Sciences's geographical spatial data cloud station
Point (http://www.gscloud.cn) provides；2 > Xijiang River and its near zone Yuanjiang River, Yuxi, Kunming, Huize, Zhanyi County,
Luxi, screen side, Pan county, Xingyi, Guangnan, Wangmo County, Napo County, Anshun, the west of Guizhou Province, Bijie, Kweiyang, Luodian, Fengshan, Baise, is made tranquil at Mengzi
West, Longzhou, Pingguo, Du Shan, Kerry, rivers and ponds, all peace, Sansui, Rongjiang, Nanning, Qiezhou, Fang Cheng, Dongxing, channel, Rong'an, willow
State, guest, the Lingshan, Guilin, Mengshan, Guiping, Yulin, Dao County, He County, Wuzhou, Luoding, letter a surname, Lian County, Guangning, height want, Guangzhou
Deng 51 2000~2013 annual precipitation data of meteorological site and year temperature record；
Step 2, according to during 2000 to 2013 be collected into, spatial resolution is 500 meters × 500 meters, is divided into
10 days NDVI data pass through ArcGIS software Spatial Analyst Tools module Local tool Cell
Statistics gradually obtains the moon, season NDVI time series raster data according to maximum value synthetic method, finally obtains a year NDVI
Time series raster data, then pass through ArcGIS software Spatial Analyst Tools module Map Algebra tool
Year NDVI time series raster data is converted to vegetation coverage time series raster data by Raster Calculator；Root
According to weather station year precipitation, year temperature record is collected into, pass through Spatial Analyst Tools module I nterpolation work
Tool Kriging space interpolation is calculated and the research area annual rainfall of vegetation coverage same accuracy and year temperature time series
Raster data；
Step 3, according to the year vegetation coverage time series raster data being calculated, pass through ArcGIS software
Spatial Analyst Tools module Local tool Combine is by 2000-2013 vegetation coverage time series grid
The stacked data new raster file of generation added together is simultaneously named as n2000-2013, and newly-generated raster file attribute list is exported
At txt file, the i.e. numeric data of textual form；Pass through ArcGIS software Conversion Tool module From Raster work
Having Raster to point, rainfall, temperature time series raster data are converted into dot file year by year by 2000-2013, then
Dot file is exported to annual rainfall time series numeric data, the year temperature time series numeric data in textual form respectively；
Three kinds of data texts are respectively designated as vegetation coverage .txt, annual rainfall .txt, year temperature .txt；
Step 4, on the basis of step 3, by vegetation coverage .txt, annual rainfall .txt, year temperature .txt tri- number of files
Computer is read according to by R language, vegetative coverage is calculated to the lag of the time-lag effect of precipitation and temperature by R Programming with Pascal Language
The time limit, and result data is saved with text file, file is respectively designated as Np.txt, Nt.txt, passes through ArcGIS software
Two files are linked to two new fields of formation on the n2000-2013 raster file generated in step 3 respectively, are led to respectively
It crosses ArcGIS software Look up tool and two newer fields is generated into two new raster datas and drafting pattern, such as Fig. 1 and Fig. 2
It is shown, vegetative coverage is respectively designated as to the lag time limit of the time-lag effect of precipitation, vegetative coverage to the time-lag effect of temperature
Lag the time limit；
Step 5, on the basis of step 3, by vegetation coverage .txt, annual rainfall .txt, year temperature .txt tri- number of files
Computer is read according to by R language, vegetative coverage is calculated by R Programming with Pascal Language, what lag year prescribed a time limit is reached to precipitation and temperature
Correlation, and result data is saved with text file, file is respectively designated as Rp.txt, Rp.txt, soft by ArcGIS
Two files are linked to two new fields of formation on the n2000-2013 raster file generated in step 3 by part respectively, respectively
Two newer fields are generated into two new raster datas and drafting pattern by ArcGIS software Look up tool, such as Fig. 3 and figure
Shown in 4, be respectively designated as vegetative coverage to precipitation reach lag year prescribe a time limit correlation, vegetative coverage to temperature reach lag year
Correlation in limited time；
Step 6, on the basis of step 3, by vegetation coverage .txt, annual rainfall .txt, year temperature .txt tri- number of files
Computer is read according to by R language, when most strong time-lag effect of the vegetative coverage to precipitation and temperature is calculated by R Programming with Pascal Language
The lag time limit, and by result data save with text file in, file is respectively designated as Qnp.txt, Qnt.txt, passes through
Two files are linked to two new words of formation on the n2000-2013 raster file generated in step 3 by ArcGIS software respectively
Two newer fields are generated two new raster datas and drafting pattern by ArcGIS software Look up tool respectively, such as by section
Shown in Fig. 5 and Fig. 6, the lag time limit, vegetative coverage when being respectively designated as most strong time-lag effect of the vegetative coverage to precipitation are to gas
Lag time limit when the most strong time-lag effect of temperature；
Step 7, on the basis of step 3, by vegetation coverage .txt, annual rainfall .txt, year temperature .txt tri- number of files
Computer is read according to by R language, when most strong time-lag effect of the vegetative coverage to precipitation and temperature is calculated by R Programming with Pascal Language
Correlation, and by result data save with text file in, file is respectively designated as Qrp.txt, Qrt.txt, passes through
Two files are linked to two new words of formation on the n2000-2013 raster file generated in step 3 by ArcGIS software respectively
Two newer fields are generated two new raster datas and drafting pattern by ArcGIS software Look up tool respectively, such as by section
Shown in Fig. 7 and Fig. 8, the correlation, vegetative coverage when being respectively designated as most strong time-lag effect of the vegetative coverage to precipitation are to temperature
Most strong time-lag effect when correlation.
Claims (7)
1. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect, which is characterized in that specifically according to
Lower step is implemented:
Step 1, it determines survey region, collects weather station year precipitation data, year temperature record and year NDVI remotely-sensed data；
Step 2, according to the year NDVI remotely-sensed data being collected into, a year vegetation coverage time series raster data, root is calculated
According to weather station year precipitation data, year temperature record is collected into, it is calculated by space interpolation same as year vegetation coverage smart
The research area annual rainfall time series raster data and year temperature time series raster data of degree；
It step 3, will vegetation coverage time series raster data, year by year year by year by ArcGIS software on the basis of step 2
Rainfall time series raster data and temperature time series raster data is separately converted to point data year by year, then pass through ArcGIS
Software respectively exports the attribute list of time series dot file, obtain year by year vegetation coverage time series, year by year rainfall when
Between sequence and the numeric data of temperature time series textual form year by year；
Step 4, it on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, year by year drops
The numeric data of rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through computer
Programming, calculates separately the lag time limit for obtaining vegetative coverage to the time-lag effect of precipitation and temperature, by vegetative coverage to precipitation and
The lag time limit of the time-lag effect of temperature is respectively converted into raster data；
Step 5, it on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, year by year drops
The numeric data of rainfall time series textual form and the year by year numeric data of temperature time series textual form pass through computer
Programming calculates separately to obtain vegetative coverage and reaches the correlation prescribed a time limit in lag year to precipitation and temperature, calculated result vegetation is covered
Lid reaches the correlation that lag year prescribes a time limit to precipitation and temperature and is respectively converted into raster data；
Step 6, it on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, year by year drops
The numeric data of rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through computer
Programming, calculates separately to obtain lag time limit when most strong time-lag effect of the vegetative coverage to precipitation and temperature, calculated result is planted
Lag time limit when being capped the most strong time-lag effect to precipitation and temperature is respectively converted into raster data；
Step 7, it on the basis of step 3, according to the numeric data of vegetation coverage time series textual form year by year, year by year drops
The numeric data of rainfall time series textual form and the year by year numeric data of temperature time series textual form, pass through computer
Programming, calculates separately to obtain correlation when most strong time-lag effect of the vegetative coverage to precipitation and temperature, by calculated result vegetation
Correlation when covering the most strong time-lag effect to precipitation and temperature is respectively converted into raster data.
2. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect according to claim 1,
It being characterized in that, in the step 1, year NDVI remotely-sensed data is obtained using maximum value synthetic method, specifically:
By ArcGIS software Spatial Analyst Tools module Local tool Cell Statistics according to maximum value
Synthetic method gradually obtains the moon, season NDVI time series raster data, finally obtains a year NDVI time series raster data, then lead to
ArcGIS software Spatial Analyst Tools module Map Algebra tool Raster Calculator is crossed by year NDVI
Time series raster data is converted to vegetation coverage time series raster data.
3. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect according to claim 1,
It is characterized in that, the step 2 specifically:
Step 2.1, year vegetation coverage data pass through ArcGIS software Spatial Analyst Tools mould by NDVI data
When year NDVI time series raster data is converted to vegetation coverage by block Map Algebra tool Raster Calculator
Between sequence trellis data；It is calculated using formula (1) formula:
F=(NDVI-NDVImin)/(NDVImax-NDVImin) (1)；
In formula (1), f is vegetation coverage, and NDVI is the vegetation index of pixel, NDVImaxAnd NDVIMinIt is research area NDVI respectively
Maximum value and minimum value；
Step 2.2, according to weather station year precipitation data, year temperature record is collected into, pass through ArcGIS software Spatial
In Analyst Tools module gram in golden spatial interpolation methods calculate and dropped with the research area of year vegetation coverage same accuracy year
Rainfall time series raster data and year temperature time series raster data.
4. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect according to claim 1,
Be characterized in that, in the step 4, year vegetative coverage to the calculation method of the time lag time limit of rainfall and temperature, specifically:
Step 4.1, by R language to the number of the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Value Data carries out Pearson came correlation test, if inspection result is significant, grid year, vegetative coverage was to time lag year of rainfall
It is limited to 0 year；If inspection result is not significant, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year plant
The numeric data of capped time series textual form carries out Pearson came correlation test, if inspection result is significant, the grid
Year vegetative coverage is limited to the time lag year of rainfall 1 year；If inspection result is not still significant, year corresponding to the same grid
Rainfall, postpone N year vegetative coverage time series textual form numeric data carry out Pearson came correlation test, postpone N
Until inspection result is significant after year, then grid year vegetative coverage is limited to N to the time lag year of rainfall, and wherein N is up to the time
The length of sequence subtracts one；If inspection result is not significant always, grid year vegetative coverage time-lag effect is not present to rainfall；
Step 4.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series text
The numeric data of form carries out the calculating such as step 4.1, obtain each grid year vegetative coverage to the time lag time limit of rainfall；
Step 4.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form numerical value
Data are carried out the calculating such as step 4.2, obtain each grid year vegetative coverage to the time lag time limit of temperature；
Step 4.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
5. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect according to claim 1,
Be characterized in that, in the step 5, year vegetative coverage to rainfall and temperature reach lag year prescribe a time limit correlation calculation method,
Specifically:
Step 5.1, by R language to the number of the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Value Data carries out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage lag is reached to rainfall
The correlation that year prescribes a time limit is the related coefficient of Pearson came correlation test；If inspection result is not significant, by R language to same
The corresponding annual rainfall of one grid, postpone 1 year year vegetative coverage time series textual form numeric data carry out Pearson came
Correlation test, if inspection result is significant, grid year vegetative coverage to reach the correlation that lag year prescribes a time limit to rainfall be skin
The related coefficient of your inferior correlation test；If inspection result is not still significant, to the corresponding annual rainfall of the same grid, push away
Afterwards N year vegetative coverage time series textual form numeric data carry out Pearson came correlation test, postpone after N until
Inspection result is significant, then it is Pearson came correlation test that grid year vegetative coverage, which reaches the correlation that lag year prescribes a time limit to rainfall,
Related coefficient, the length that wherein N is up to time series subtracts one；If inspection result is not significant always, grid year vegetation
Time-lag effect is not present to rainfall in covering；
Step 5.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series text
The numeric data of form carries out the calculating of step 5.1, and the then grid year vegetative coverage for obtaining each grid reaches stagnant to rainfall
The correlation that the year after next prescribes a time limit；
Step 5.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form numerical value
Data carry out the calculating such as step 5.2, obtain each grid year vegetative coverage and reach the correlation prescribed a time limit in lag year to temperature；
Step 5.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
6. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect according to claim 1,
Be characterized in that, in the step 6, year vegetative coverage to rainfall and temperature most strong time-lag effect when lag the time limit calculating side
Method, specifically:
Step 6.1, by R language to the number of the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Value Data carries out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage lag is reached to rainfall
Correlation year in limited time is the related coefficient of Pearson came correlation test, then the time lag time limit of grid year vegetative coverage to rainfall
It is 0 year；Then, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series text
The numeric data of form carries out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
The correlation prescribed a time limit to lag year is the related coefficient of Pearson came correlation test, then grid year vegetative coverage to rainfall when
It is limited in stagnant year 1 year；Then, to the corresponding annual rainfall of the same grid, postpone the year vegetative coverage time series textual form of N
Numeric data carry out Pearson came correlation test, postpone after N until inspection result is significant, then grid year vegetative coverage pair
Rainfall reaches the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test, then grid year vegetative coverage is to drop
The time lag year of rain is limited to N, and the length that wherein N is up to time series subtracts one；Finally, finding the grid in N number of calculated result
Year vegetative coverage rainfall occurs the corresponding time lag time limit of maximum related coefficient when hysteresis effect, then is limited to the grid year year
Lag time limit when vegetative coverage is to rainfall most strong time-lag effect；If inspection result is not significant always, grid year vegetation cover
Time-lag effect is not present to rainfall in lid；
Step 6.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series text
The numeric data of form carries out the calculating of step 6.1, when obtaining each grid year vegetative coverage to rainfall most strong time-lag effect
Lag the time limit；
Step 6.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form numerical value
Data carry out the calculating such as step 6.2, lag time limit when to each grid year vegetative coverage to temperature most strong time-lag effect；
Step 6.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
7. the analysis method of a kind of regional vegetation covering variation and climatic factor time-lag effect according to claim 1,
Be characterized in that, in step 7, year vegetative coverage to rainfall and temperature most strong time-lag effect when correlation calculation method, specifically
Are as follows:
Step 7.1, by R language to the number of the corresponding annual rainfall of the same grid, year vegetative coverage time series textual form
Value Data carries out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage lag is reached to rainfall
Correlation year in limited time is the related coefficient of Pearson came correlation test, then the time lag time limit of grid year vegetative coverage to rainfall
It is 0 year；Then, by R language to the corresponding annual rainfall of the same grid, postpone 1 year year vegetative coverage time series text
The numeric data of form carries out Pearson came correlation test, if inspection result is significant, grid year vegetative coverage rainfall is reached
The correlation prescribed a time limit to lag year is the related coefficient of Pearson came correlation test, then grid year vegetative coverage to rainfall when
It is limited in stagnant year 1 year；Then, to the corresponding annual rainfall of the same grid, postpone the year vegetative coverage time series textual form of N
Numeric data carry out Pearson came correlation test, postpone after N until inspection result is significant, then grid year vegetative coverage pair
Rainfall reaches the related coefficient that the correlation that lag year prescribes a time limit is Pearson came correlation test, then grid year vegetative coverage is to drop
The time lag year of rain is limited to N, and the length that wherein N is up to time series subtracts one；Finally, finding the grid in N number of calculated result
Year vegetative coverage rainfall occurs maximum related coefficient when hysteresis effect, then the related coefficient is grid year vegetative coverage
Correlation when to rainfall most strong time-lag effect；If inspection result is not significant always, grid year vegetative coverage to rainfall not
There are time-lag effects；
Step 7.2, the annual rainfall by R Programming with Pascal Language to the textual form of all grids, year vegetative coverage time series text
The numeric data of form carries out the calculating of step 7.1, when obtaining each grid year vegetative coverage to rainfall most strong time-lag effect
Correlation；
Step 7.3, by R Programming with Pascal Language to textual form year temperature, year vegetative coverage time series textual form numerical value
Data carry out the calculating such as step 7.2, obtain correlation of each grid year vegetative coverage to temperature most strong time-lag effect when；
Step 7.4, the calculated result of textual form numeric format is converted to by raster data by ArcGIS software.
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